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Publication
Human-Robot Collaboration by Intention recognition using Probabilistic State MachinesMuhammad Awais , Dominik Henrich
Abstract (english)
Combining the intelligent and situation dependent decision making capabilities of a human with the accuracy and power of a robot, performance of many tasks can be improved. The human-robot collaboration scenarios are increasing. Human-robot interaction is not only restricted to the humanoid robots interacting with the humans or to the mobile service robots providing different services but also industrial robots opens a wide range of human-robot collaboration set-ups. Intention recognition plays a key role in intuitive human-robot collaboration. In this paper we present a novel approach for recognizing the human intention using weighted probabilistic state machines. We categorize the recognition task into two categories namely explicit and implicit intention communication. We present a general intention recognition approach that can be applied to any human-robot cooperation situation. The algorithm is tested with an industrial robotic arm.
Publication data
| Year: | 2010 |
| Publication date: | 23. June 2010 |
| Source: | 19th IEEE International Workshop on Robotics in Alpe-Adria-Danube Region - RAAD 2010, 23-25 June 2010, Budapest, Hungary |
| Referrer: | https://www.ai3.uni-bayreuth.de/de/publikationen/resypub/index.php?mode=pub_show&pub_ref=awais2010a |
BibTeX
@ARTICLE{awais2010a,
TITLE = "Human-Robot Collaboration by Intention recognition using Probabilistic State Machines",
AUTHOR = "Awais, Muhammad and Henrich, Dominik",
YEAR = "2010",
JOURNAL = "19th IEEE International Workshop on Robotics in Alpe-Adria-Danube Region - RAAD 2010, 23-25 June 2010, Budapest, Hungary",
HOWPUBLISHED = "\url{https://www.ai3.uni-bayreuth.de/de/publikationen/resypub/index.php?mode=pub_show&pub_ref=awais2010a}",
}
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| Filename | Size | Language | Format | ||||
|---|---|---|---|---|---|---|---|
| awais2010a.HumanRobot.Collaboration.by.Intention.recognition .using.Probabilistic.State.Machines.pdf |
936.4K | english | download preprint |